To survive in this COVID-19 age where companies are deliberately looking for the solutions that may help them deal with the business challenges, cloud computing and its associated tools prepare them with cost-effective strategies.
The term Machine Learning was given by Arthur Samuel in the year 1959 and he defined it as the “field of study that gives computers the ability to learn without being explicitly programmed”. Now Machine Learning is helping organizations to take data driven decisions rather completely relying on the experience driven decisions.
Further in this article, we will discuss the benefits of Machine Learning in the development process of a mobile app and everything which revolves around Machine Learning and its use in developing a mobile app.
As Machine Learning and Artificial Intelligence (AI) are making their ways in the market there will be a time surely when there will not arise any requirement of developers and IT professionals. The technical bugs will get fixed by the machines themselves. Lot of manual work will be automated.
In the field of Machine Learning, logistic regression is still the top choice for classification problems. It is simple yet efficient algorithm which produces accurate models in most of the cases. In its basic form, it uses the logistic function to calculate the probability score which helps to classify the binary dependent variable to its respective class. Logistic regression is the transformed form of the linear regression. In this post I have explained the end to end step involved in the classification machine learning problems using the logistic regression and also performed the detailed analysis of the model output with various performance parameters.
Artificial neural networks (ANNs), usually called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains. There is lot of hype these days regarding the Artificial Intelligence and its technologies.
In this article, we will talk about the Hype vs Reality on AI technologies and also will explain about the various terminology associated with Artificial Intelligence (AI), Transitioning from Machine Learning to Deep Learning, Basic building blocks for the study of AI and Artificial Neural Network (ANN).
In this post, we will develop a classification model where we’ll try to classify the movie reviews on positive and negative classes. I have used different machine learning algorithm to train the model and compared the accuracy of those models at the end. you can keep this post as a template to use various machine learning algorithms in python for text classification.
At the end we will validate the model by passing a random review to the trained model and understand the output class predicted by the model. You will learn how to create and use the pipeline for numerical feature extraction and model training together as a one function.